Abstract
Protein structures are made up of periodic and aperiodic structural elements (i.e., α-helices, β-strands and loops). Despite the apparent lack of regular structure, loops have specific conformations and play a central role in the folding, dynamics, and function of proteins. In this article, we reviewed our previous works in the study of protein loops as local supersecondary structural motifs or Smotifs. We reexamined our works about the structural classification of loops (ArchDB) and its application to loop structure prediction (ArchPRED), including the assessment of the limits of knowledge-based loop structure prediction methods. We finalized this article by focusing on the modular nature of proteins and how the concept of Smotifs provides a convenient and practical approach to decompose proteins into strings of concatenated Smotifs and how can this be used in computational protein design and protein structure prediction.
Acknowledgments
This article is partially based on our previous publications [ref. 30–39, 41]. NFF acknowledges support from ACCIO, Generalitat of Catalunya under the TecnioSpring Program, project number TECSPR13-1-0008, REA grant agreement 600388. This work was supported by NIH grants GM094665 and GM096041 to AF. JB and BO acknowledge support from the Spanish Ministry of Economy under grant BIO2011-22568.
Author contributions: All authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Leszczynski JF, Rose GD. Loops in globular proteins: a novel category of secondary structure. Science 1986;234:849–55.10.1126/science.3775366Search in Google Scholar
2. Krishna MM, Lin Y, Rumbley JN, Walter Englander S. Cooperative omega loops in cytochrome c: role in folding and function. J Mol Biol 2003;331:29–36.10.1016/S0022-2836(03)00697-1Search in Google Scholar
3. Collinet B, Garcia P, Minard P, Desmadril M. Role of loops in the folding and stability of yeast phosphoglycerate kinase. Eur J Biochem 2001;268:5107–18.10.1046/j.0014-2956.2001.02439.xSearch in Google Scholar PubMed
4. Linhananta A, Zhou H, Zhou Y. The dual role of a loop with loop contact distance in folding and domain swapping. Protein Sci 2002;11:1695–701.10.1110/ps.0205002Search in Google Scholar PubMed PubMed Central
5. Fersht AR. Transition-state structure as unifying basis in protein-folding mechanisms: contact order, chain topology, stability, and the extended nucleus mechanism. Proc Natl Acad Sci 2000;15:1525–9.10.1073/pnas.97.4.1525Search in Google Scholar PubMed PubMed Central
6. Ulfer R, Kirschner K. The importance of surface loops for stabilizing an eightfold beta alpha protein. Protein Sci 1992; 1:31–45.10.1002/pro.5560010105Search in Google Scholar PubMed PubMed Central
7. Batori V, Koide A, Koide S. Exploring the potential of the monobody scaffold: effects of loop elongation on the stability of a fibronectin type III domain. Protein Eng 2002;15: 1015–20.10.1093/protein/15.12.1015Search in Google Scholar PubMed
8. Kumar S, Nussinov R. How do thermophilic proteins deal with heat? Cell Mol Life Sci 2001;58:1216–33.10.1007/PL00000935Search in Google Scholar PubMed
9. Zhou HX. Loops, linkage, rings, catenanes, cages, and crowders: entropy-based strategies for stabilizing proteins. Acc Chem Res 2004;37:123–30.10.1021/ar0302282Search in Google Scholar PubMed
10. Hoedemaeker FJ, van Eijsden RR, Diaz CL, de Pater BS, Kijne JW. Destabilization of pea lectin by substitution of a single amino acid in a surface loop. Plant Mol Biol 1993;22: 1039–46.10.1007/BF00028976Search in Google Scholar PubMed
11. Fetrow JS. Omega loops: nonregular secondary structure significant in protein function and stability. FASEB 1995;9: 708–17.10.1096/fasebj.9.9.7601335Search in Google Scholar
12. Saraste M, Sibbald PR, Wittinghofer A. The P-loop – a common motif in ATP- and GTP-binding proteins. Trends Biochem Sci 1990;15:430–4.10.1016/0968-0004(90)90281-FSearch in Google Scholar
13. Chen LT, Liang WX, Chen S, Li RK, Tan JL, Xu PF, et al. Functional and molecular features of the calmodulin-interacting protein IQCG required for haematopoiesis in zebrafish. Nat Commun 2014;5:3811.10.1038/ncomms4811Search in Google Scholar
14. Kawasaki H, Kretsinger RH. Calcium-binding proteins 1: EF-hands. Protein Profile 1995;2:297–490.Search in Google Scholar
15. Wlodawer A, Miller M, Jaskolski M, Sathyanarayana BK, Baldwin E, Weber IT, et al. Conserved folding in retroviral proteases: crystal structure of a synthetic HIV-1 protease. Science 1989;245:616–21.10.1126/science.2548279Search in Google Scholar
16. Johnson LN, Lowe ED, Noble ME, Owen DJ. The Eleventh Datta Lecture. The structural basis for substrate recognition and control by protein kinases. FEBS Lett 1998;430:1–11.10.1016/S0014-5793(98)00606-1Search in Google Scholar
17. Gunasekaran K, Ma B, Nussinov R. Triggering loops and enzyme function: identification of loops that trigger and modulate movements. J Mol Biol 2003;332:143–59.10.1016/S0022-2836(03)00893-3Search in Google Scholar
18. Zgiby S, Plater AR, Bates MA, Thomson GJ, Berry A. A functional role for a flexible loop containing Glu182 in the class II fructose-1,6-biphosphate aldolase from Escherichia coli. J Mol Biol 2002;315:131–40.10.1006/jmbi.2001.5237Search in Google Scholar
19. Adams JA. Activation loop phosphorylation and catalysis in protein kinases: is there functional evidence for the autoinhibitor model? Biochemistry 2003;42:601–7.10.1021/bi020617oSearch in Google Scholar
20. Johnson LN, Noble ME, Owen DJ. Active and inactive protein kinases: structural basis for regulation. Cell 1996;85:149–58.10.1016/S0092-8674(00)81092-2Search in Google Scholar
21. Joseph D, Petsko GA, Karplus M. Anatomy of a conformational change: hinged “lid” motion of the triosephosphate isomerase loop. Science 1990;249:1425–8.10.1126/science.2402636Search in Google Scholar PubMed
22. Sinev MA, Sineva EV, Ittah V, Haas E. Domain closure in adenylate kinase. Biochemistry 1996;35:6425–37.10.1021/bi952687jSearch in Google Scholar
23. Fritz-Wolf K, Schnyder T, Wallimann T, Kabsch W. Structure of mitochondrial creatine kinase. Nature 1996;381:341–5.10.1038/381341a0Search in Google Scholar
24. Feng W, Shi Y, Li M, Zhang M. Tandem PDZ repeats in glutamate receptor-interacting proteins have a novel mode of PDZ domain-mediated target binding. Nat Struct Biol 2003;10:972–8.10.1038/nsb992Search in Google Scholar
25. Planas-Iglesias J, Bonet J, Garcia-Garcia J, Marin-Lopez MA, Feliu E, Oliva B. Understanding protein-protein interactions using local structural features. J Mol Biol 2013;425:1210–24.10.1016/j.jmb.2013.01.014Search in Google Scholar
26. Tainer JA, Thayer MM, Cunningham RP. DNA repair proteins. Curr Opin Struct Biol 1995;5:20–6.10.1016/0959-440X(95)80005-LSearch in Google Scholar
27. Kim ST, Shirai H, Nakajima N, Higo J, Nakamura H. Enhanced conformational diversity search of CDR-H3 in antibodies: role of the first CDR-H3 residue. Proteins 1999;37:683–96.10.1002/(SICI)1097-0134(19991201)37:4<683::AID-PROT17>3.0.CO;2-DSearch in Google Scholar
28. Bernstein LS, Ramineni S, Hague C, Cladman W, Chidiac P, Levey AI, et al. RGS2 binds directly and selectively to the M1 muscarinic acetylcholine receptor third intracellular loop to modulate Gq/11alpha signaling. J Biol Chem 2004;279:21248–56.10.1074/jbc.M312407200Search in Google Scholar
29. Zomot E, Kanner BI. The interaction of the gamma-aminobutyric acid transporter GAT-1 with the neurotransmitter is selectively impaired by sulfhydryl modification of a conformationally sensitive cysteine residue engineered into extracellular loop IV. J Biol Chem 2003;278:42950–8.10.1074/jbc.M209307200Search in Google Scholar
30. Fernandez-Fuentes N, Hermoso A, Espadaler J, Querol E, Aviles FX, Oliva B. Classification of common functional loops of kinase super-families. Proteins 2004;56:539–55.10.1002/prot.20136Search in Google Scholar
31. Espadaler J, Fernandez-Fuentes N, Hermoso A, Querol E, Aviles FX, Sternberg MJ, et al. ArchDB: automated protein loop classification as a tool for structural genomics. Nucleic Acids Res 2004;32:D185–8.10.1093/nar/gkh002Search in Google Scholar
32. Hermoso A, Espadaler J, Enrique Querol E, Aviles FX, Sternberg MJ, Oliva B, et al. Including functional annotations and extending the collection of structural classifications of protein loops (ArchDB). Bioinformatics Biol Insights 2009;1:77–90.Search in Google Scholar
33. Oliva B, Bates PA, Querol E, Aviles FX, Sternberg MJ. Automated classification of antibody complementarity determining region 3 of the heavy chain (H3) loops into canonical forms and its application to protein structure prediction. J Mol Biol 1998;279:1193.10.1006/jmbi.1998.1847Search in Google Scholar PubMed
34. Oliva B, Bates PA, Querol E, Aviles FX, Sternberg MJ. An automated classification of the structure of protein loops. J Mol Biol 1997;266:814.10.1006/jmbi.1996.0819Search in Google Scholar PubMed
35. Bonet J, Planas-Iglesias J, Garcia-Garcia J, Marin-Lopez MA, Fernandez-Fuentes N, Oliva B. ArchDB 2014: structural classification of loops in proteins. Nucleic Acids Res 2014;42:D315–9.10.1093/nar/gkt1189Search in Google Scholar PubMed PubMed Central
36. Fernandez-Fuentes N, Querol E, Aviles FX, Sternberg MJ, Oliva B. Prediction of the conformation and geometry of loops in globular proteins: testing ArchDB, a structural classification of loops. Proteins 2005;60:746–57.10.1002/prot.20516Search in Google Scholar PubMed
37. Fernandez-Fuentes N, Oliva B, Fiser A. A supersecondary structure library and search algorithm for modeling loops in protein structures. Nucleic Acids Res 2006;34:2085–97.10.1093/nar/gkl156Search in Google Scholar PubMed PubMed Central
38. Fernandez-Fuentes N, Fiser A. Saturating representation of loop conformational fragments in structure databanks. BMC Struct Biol 2006;6:15.10.1186/1472-6807-6-15Search in Google Scholar PubMed PubMed Central
39. Fernandez-Fuentes N, Dybas JM, Fiser A. Structural characteristics of novel protein folds. PLoS Comput Biol 2010;6:e1000750.10.1371/journal.pcbi.1000750Search in Google Scholar PubMed PubMed Central
40. Menon V, Vallat BK, Dybas JM, Fiser A. Modeling proteins using a supersecondary structure library and NMR chemical shift information. Structure 2013;21:891–9.10.1016/j.str.2013.04.012Search in Google Scholar PubMed PubMed Central
41. Bonet J, Segura J, Planas-Iglesias J, Oliva B, Fernandez-Fuentes N. Frag‘r’Us: knowledge-based sampling of protein backbone conformations for de novo structure-based protein design. Bioinformatics 2014;30:1935–6.10.1093/bioinformatics/btu129Search in Google Scholar PubMed
42. Venkatachalam CM. Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units. Biopolymers 1968;6:1425–36.10.1002/bip.1968.360061006Search in Google Scholar PubMed
43. Richardson JS. The anatomy and taxonomy of protein structure. Adv Protein Chem 1981;34:167–339.10.1016/S0065-3233(08)60520-3Search in Google Scholar
44. Wilmot CM, Thornton J. Analysis and prediction of the different types of beta-turns in proteins. J Mol Biol 1988;203:221–32.10.1016/0022-2836(88)90103-9Search in Google Scholar
45. Hutchinson EG, Thornton JM. A revised set of potentials for beta-turn formation in proteins. Protein Sci 1994;3:2207–16.10.1002/pro.5560031206Search in Google Scholar
46. Matthews BW. The gamma turn. Evidence for a new folded conformation in proteins. Macromolecules 1972;5:818–9.10.1021/ma60030a031Search in Google Scholar
47. Milner-White E, Ross BM, Ismail R, Belhadj-Mostefa K, Poet R. One type of gamma-turn, rather than the other gives rise to chain-reversal in proteins. J Mol Biol 1988;204:777–82.10.1016/0022-2836(88)90368-3Search in Google Scholar
48. Rose GD, Gierasch LM, Smith JA. Turns in peptides and proteins. Adv Protein Chem 1985;37:1–109.10.1016/S0065-3233(08)60063-7Search in Google Scholar
49. Sibanda BL, Thornton JM. Conformation of beta hairpins in protein structures: classification and diversity in homologous structures. Methods Enzymol 1991;202:59.10.1016/0076-6879(91)02007-VSearch in Google Scholar
50. Sibanda BL, Blundell TL, Thornton JM. Conformation of beta-hairpins in protein structures. A systematic classification with applications to modelling by homology, electron density fitting and protein engineering. J Mol Biol 1989;206:759.10.1016/0022-2836(89)90583-4Search in Google Scholar
51. Milner-White EJ, Poet R. Four classes of beta-hairpins in proteins. Biochem J 1986;240:289–92.10.1042/bj2400289Search in Google Scholar
52. Efimov AV. Structure of coiled beta-beta-hairpins and beta-beta-corners. FEBS Lett 1991;284:288–92.10.1016/0014-5793(91)80706-9Search in Google Scholar
53. Edwards M, Sternberg MJ, Thornton J. Structural and sequence patterns in the loops of beta alpha beta units. Prot Eng 1987;1:173–81.10.1093/protein/1.3.173Search in Google Scholar PubMed
54. Rice PA, Goldman A, Steitz TA. A helix-turn-strand structural motif common in alpha-beta proteins. Prot Struct Funct Genet 1990;8:343.10.1002/prot.340080407Search in Google Scholar PubMed
55. Donate LE, Rufino SD, Canard LH, Blundell TL. Conformational analysis and clustering of short and medium size loops connecting regular secondary structures: a database for modeling and prediction. Protein Sci 1996;5:2600.10.1002/pro.5560051223Search in Google Scholar PubMed PubMed Central
56. Wintjens RT, Rooman MJ, Wodak SJ. Automatic classification and analysis of alpha alpha-turn motifs in proteins. J Mol Biol 1996;255:235–53.10.1006/jmbi.1996.0020Search in Google Scholar PubMed
57. Regad L, Martin J, Nuel G, Camproux AC. Mining protein loops using a structural alphabet and statistical exceptionality. BMC Bioinformatics 2010;11:75.10.1186/1471-2105-11-75Search in Google Scholar PubMed PubMed Central
58. Shen Y, Picord G, Guyon F, Tuffery P. Detecting protein candidate fragments using a structural alphabet profile comparison approach. PLoS ONE 2013;8:e80493.10.1371/journal.pone.0080493Search in Google Scholar PubMed PubMed Central
59. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res 2000;28:235–42.10.1093/nar/28.1.235Search in Google Scholar PubMed PubMed Central
60. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983;22:2577–637.10.1002/bip.360221211Search in Google Scholar PubMed
61. Everitt B. Cluster analysis. Chapter 3. Heineman Educational Books Ltd., London, 1974.Search in Google Scholar
62. Gene Ontology Consortium. The Gene Ontology: enhancements for 2011. Nucleic Acids Res 2012;40:D559–64.10.1093/nar/gkr1028Search in Google Scholar PubMed PubMed Central
63. Bairoch A. The ENZYME database in 2000. Nucleic Acids Res 2000;28:304–5.10.1093/nar/28.1.304Search in Google Scholar PubMed PubMed Central
64. Hubbard TJ, Murzin AG, Brenner SE, Chothia C. SCOP: a structural classification of proteins database. Nucleic Acids Res 1997;25:236.10.1093/nar/25.1.236Search in Google Scholar
65. Espadaler J, Querol E, Aviles FX, Oliva B. Identification of function-associated loop motifs and application to protein function prediction. Bioinformatics 2006;22:2237–43.10.1093/bioinformatics/btl382Search in Google Scholar
66. Van Dongen S. Graph clustering via a discrete uncoupling process. SIAM J Matrix Anal Appl 2008;30:121–41.10.1137/040608635Search in Google Scholar
67. Baker D, Sali A. Protein structure prediction and structural genomics. Science 2001;294:93–6.10.1126/science.1065659Search in Google Scholar
68. Fiser A, Feig M, Brooks CL III, Sali A. Evolution and physics in comparative protein structure modeling. Acc Chem Res 2002;35:413.10.1021/ar010061hSearch in Google Scholar
69. Pieper U, Eswar N, Braberg H, Madhusudhan MS, Davis FP, Stuart AC, et al. MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 2004;32:D217.10.1093/nar/gkh095Search in Google Scholar
70. Eiben CB, Siegel JB, Bale JB, Cooper S, Khatib F, Shen BW, et al. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nat Biotechnol 2012;30:190–2.10.1038/nbt.2109Search in Google Scholar
71. Fernandez-Fuentes N, Fiser A. Modeling loops in protein structures. In: Introduction to protein structure prediction: methods and algorithms. Wiley Series on Bioinformatics. New Jersey: John Wiley & Sons, Inc., 2010:279–299.Search in Google Scholar
72. Greer J. Comparative model-building of the mammalian serine proteases. J Mol Biol 1981;153:1027.10.1016/0022-2836(81)90465-4Search in Google Scholar
73. Jones TA, Thirup S. Using known substructures in protein model building and crystallography. EMBO J 1986;5:819.10.1002/j.1460-2075.1986.tb04287.xSearch in Google Scholar PubMed PubMed Central
74. Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, et al. Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr D Biol Crystallogr 1998;54:1078–84.10.1107/S0907444998009378Search in Google Scholar
75. Chothia C, Lesk AM. Canonical structures for the hypervariable regions of immunoglobulins. J Mol Biol 1987;196:901.10.1016/0022-2836(87)90412-8Search in Google Scholar
76. Kwasigroch JM, Chomilier J, Mornon JP. A global taxonomy of loops in globular proteins. J Mol Biol 1996;259:855–72.10.1006/jmbi.1996.0363Search in Google Scholar PubMed
77. Wojcik J, Mornon JP, Chomilier J. New efficient statistical sequence-dependent structure prediction of short to medium-sized protein loops based on an exhaustive loop classification. J Mol Biol 1999;255:235–53.10.1006/jmbi.1999.2826Search in Google Scholar PubMed
78. Martin AC, Thornton JM. Structural families in loops of homologous proteins: automatic classification, modelling and application to antibodies. J Mol Biol 1996;263:800.10.1006/jmbi.1996.0617Search in Google Scholar PubMed
79. Burke DF, Deane CM, Blundell TL. Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure. Bioinformatics 2000;16:513.10.1093/bioinformatics/16.6.513Search in Google Scholar PubMed
80. Michalsky E, Goede A, Preissner R. Loops in Proteins (LIP) – a comprehensive loop database for homology modelling. Protein Eng 2003;16:979.10.1093/protein/gzg119Search in Google Scholar PubMed
81. Heuser P, Wohlfahrt G, Schomburg D. Efficient methods for filtering and ranking fragments for the prediction of structurally variable regions in proteins. Proteins 2004;54:583–95.10.1002/prot.10603Search in Google Scholar PubMed
82. Choi Y, Deane CM. FREAD revisited: accurate loop structure prediction using a database search algorithm. Proteins 2010;78:1431–40.10.1002/prot.22658Search in Google Scholar PubMed
83. Fernandez-Fuentes N, Zhai J, Fiser A. ArchPRED: a template based loop structure prediction server. Nucleic Acids Res 2006;34:W173–6.10.1093/nar/gkl113Search in Google Scholar PubMed PubMed Central
84. Peng HP, Yang AS. Modeling protein loops with knowledge-based prediction of sequence-structure alignment. Bioinformatics 2007;23:2836–42.10.1093/bioinformatics/btm456Search in Google Scholar PubMed
85. Ko J, Lee D, Park H, Coutsias EA, Lee J, Seok C. The FALC-Loop web server for protein loop modeling. Nucleic Acids Res 2011;39:W210–4.10.1093/nar/gkr352Search in Google Scholar
86. Fidelis K, Stern PS, Bacon D, Moult J. Comparison of systematic search and database methods for constructing segments of protein structure. Protein Eng 1994;7:953.10.1093/protein/7.8.953Search in Google Scholar
87. Lessel U, Schomburg D. Creation and characterization of a new, non-redundant fragment data bank. Protein Eng 1997;10:659.10.1093/protein/10.6.659Search in Google Scholar
88. Chance MR, Fiser A, Sali A, Pieper U, Eswar N, Xu G, et al. High-throughput computational and experimental techniques in structural genomics. Genome Res 2004;14:2145.10.1101/gr.2537904Search in Google Scholar
89. Khafizov K, Madrid-Aliste C, Almo SC, Fiser A. Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative. Proc Natl Acad Sci USA 2014;111:3733–8.10.1073/pnas.1321614111Search in Google Scholar
90. Du P, Andrec M, Levy RM. Have we seen all structures corresponding to short protein fragments in the Protein Data Bank? An update. Protein Eng 2003;16:407–14.10.1093/protein/gzg052Search in Google Scholar
91. Mezei M. Chameleon sequences in the PDB. Protein Eng 1998;11:411.10.1093/protein/11.6.411Search in Google Scholar
92. Kabsch W, Sander C. On the use of sequence homologies to predict protein structure: identical pentapeptides can have completely different conformations. Proc Natl Acad Sci USA 1984;81:1075.10.1073/pnas.81.4.1075Search in Google Scholar
93. Fernandez-Fuentes N, Fiser A. A modular perspective of protein structures: application to fragment based loop modeling. Methods Mol Biol 2013;932:141–58.10.1007/978-1-62703-065-6_9Search in Google Scholar
94. Kolaskar AS, Kulkarni-Kale U. Sequence alignment approach to pick up conformationally similar protein fragments. J Mol Biol 1992;223:1053–61.10.1016/0022-2836(92)90261-HSearch in Google Scholar
95. Shortle D. Composites of local structure propensities: evidence for local encoding of long-range structure. Protein Sci 2002;11:18–26.10.1110/ps.ps.31002Search in Google Scholar PubMed PubMed Central
96. Fiser A, Sali A. ModLoop: automated modeling of loops in protein structures. Bioinformatics 2003;19:2500.10.1093/bioinformatics/btg362Search in Google Scholar PubMed
97. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993;234:779.10.1006/jmbi.1993.1626Search in Google Scholar PubMed
98. Khare SD, Fleishman SJ. Emerging themes in the computational design of novel enzymes and protein-protein interfaces. FEBS Lett 2013;587:1147–54.10.1016/j.febslet.2012.12.009Search in Google Scholar PubMed
99. Kiss G, Celebi-Olcum N, Moretti R, Baker D, Houk KN. Computational enzyme design. Angew Chem 2013;52:5700–25.10.1002/anie.201204077Search in Google Scholar PubMed
100. Marvin JS, Hellinga HW. Conversion of a maltose receptor into a zinc biosensor by computational design. Proc Natl Acad Sci USA 2001;98:4955–60.10.1073/pnas.091083898Search in Google Scholar PubMed PubMed Central
101. Murphy PM, Bolduc JM, Gallaher JL, Stoddard BL, Baker D. Alteration of enzyme specificity by computational loop remodeling and design. Proc Natl Acad Sci USA 2009;106:9215–20.10.1073/pnas.0811070106Search in Google Scholar PubMed PubMed Central
102. Hu X, Wang H, Ke H, Kuhlman B. High-resolution design of a protein loop. Proc Natl Acad Sci USA 2007;104:17668–73.10.1073/pnas.0707977104Search in Google Scholar PubMed PubMed Central
103. Hocker B. Design of proteins from smaller fragments-learning from evolution. Curr Opin Struct Biol 2014;27C:56–62.10.1016/j.sbi.2014.04.007Search in Google Scholar PubMed
104. Yadid I, Tawfik DS. Reconstruction of functional beta-propeller lectins via homo-oligomeric assembly of shorter fragments. J Mol Biol 2007;365:10–7.10.1016/j.jmb.2006.09.055Search in Google Scholar PubMed
105. Shanmugaratnam S, Eisenbeis S, Hocker B. A highly stable protein chimera built from fragments of different folds. Protein Eng Des Sel 2012;25:699–703.10.1093/protein/gzs074Search in Google Scholar
106. Moult J, Hubbard T, Fidelis K, Pedersen JT. Critical assessment of methods of protein structure prediction (CASP): round III. Proteins 1999;Suppl 3:2–6.10.1002/(SICI)1097-0134(1999)37:3+<2::AID-PROT2>3.0.CO;2-2Search in Google Scholar
107. Claren J, Malisi C, Hocker B, Sterner R. Establishing wild-type levels of catalytic activity on natural and artificial (beta alpha)8-barrel protein scaffolds. Proc Natl Acad Sci USA 2009;106:3704–9.10.1073/pnas.0810342106Search in Google Scholar
108. Jiang L, Althoff EA, Clemente FR, Doyle L, Rothlisberger D, Zanghellini A, et al. De novo computational design of retro-aldol enzymes. Science 2008;319:1387–91.10.1126/science.1152692Search in Google Scholar
109. Wang L, Althoff EA, Bolduc J, Jiang L, Moody J, Lassila JK, et al. Structural analyses of covalent enzyme-substrate analog complexes reveal strengths and limitations of de novo enzyme design. J Mol Biol 2012;415:615–25.10.1016/j.jmb.2011.10.043Search in Google Scholar
110. Saab-Rincon G, Olvera L, Olvera M, Rudino-Pinera E, Benites E, Soberon X, et al. Evolutionary walk between (beta/alpha)(8) barrels: catalytic migration from triosephosphate isomerase to thiamin phosphate synthase. J Mol Biol 2012;416:255–70.10.1016/j.jmb.2011.12.042Search in Google Scholar
111. Azoitei ML, Correia BE, Ban YE, Carrico C, Kalyuzhniy O, Chen L, et al. Computation-guided backbone grafting of a discontinuous motif onto a protein scaffold. Science 2011;334:373–6.10.1126/science.1209368Search in Google Scholar
112. Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 2011;487:545–74.10.1016/B978-0-12-381270-4.00019-6Search in Google Scholar
113. Cooper S, Khatib F, Treuille A, Barbero J, Lee J, Beenen M, et al. Predicting protein structures with a multiplayer online game. Nature 2010;466:756–60.10.1038/nature09304Search in Google Scholar
114. Fiser A. Protein structure modeling in the proteomics era. Expert Rev Proteomics 2004;1:97–110.10.1586/14789450.1.1.97Search in Google Scholar
115. Cornilescu G, Delaglio F, Bax A. Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 1999;13:289–302.10.1023/A:1008392405740Search in Google Scholar
116. Shen Y, Delaglio F, Cornilescu G, Bax A. TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 2009;44:213–23.10.1007/s10858-009-9333-zSearch in Google Scholar
117. Bonneau R, Strauss CE, Rohl CA, Chivian D, Bradley P, Malmstrom L, et al. De novo prediction of three-dimensional structures for major protein families. J Mol Biol 2002;322:65–78.10.1016/S0022-2836(02)00698-8Search in Google Scholar
118. Bowers PM, Strauss CE, Baker D. De novo protein structure determination using sparse NMR data. J Biomol NMR 2000;18:311.10.1023/A:1026744431105Search in Google Scholar
119. Gong H, Shen Y, Rose GD. Building native protein conformation from NMR backbone chemical shifts using Monte Carlo fragment assembly. Protein Sci 2007;16:1515–21.10.1110/ps.072988407Search in Google Scholar PubMed PubMed Central
120. Shen Y, Vernon R, Baker D, Bax A. De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 2009;43:63–78.10.1007/s10858-008-9288-5Search in Google Scholar PubMed PubMed Central
121. Shen Y, Lange O, Delaglio F, Rossi P, Aramini JM, Liu G, et al. Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acad Sci USA 2008;105:4685–90.10.1073/pnas.0800256105Search in Google Scholar PubMed PubMed Central
122. Cavalli A, Salvatella X, Dobson CM, Vendruscolo M. Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 2007;104:9615–20.10.1073/pnas.0610313104Search in Google Scholar PubMed PubMed Central
123. Robustelli P, Kohlhoff K, Cavalli A, Vendruscolo M. Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins. Structure 2010;18:923–33.10.1016/j.str.2010.04.016Search in Google Scholar PubMed
124. Robustelli P, Cavalli A, Dobson CM, Vendruscolo M, Salvatella X. Folding of small proteins by Monte Carlo simulations with chemical shift restraints without the use of molecular fragment replacement or structural homology. J Phys Chem B 2009;113:7890–6.10.1021/jp900780bSearch in Google Scholar PubMed
125. Kohlhoff KJ, Robustelli P, Cavalli A, Salvatella X, Vendruscolo M. Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 2009;131:13894–5.10.1021/ja903772tSearch in Google Scholar
126. Han B, Liu YF, Ginzinger SW, Wishart DS. SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 2011;50:43–57.10.1007/s10858-011-9478-4Search in Google Scholar
127. Shen Y, Bax A. SPARTA plus: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J Biomol NMR 2010;48:13–22.10.1007/s10858-010-9433-9Search in Google Scholar
128. Meiler J. PROSHIFT: protein chemical shift prediction using artificial neural networks. J Biomol NMR 2003;26:25–37.10.1023/A:1023060720156Search in Google Scholar
129. Berjanskii M, Tang P, Liang J, Cruz JA, Zhou J, Zhou Y, et al. GeNMR: a web server for rapid NMR-based protein structure determination. Nucleic Acids Res 2009;37:W670–7.10.1093/nar/gkp280Search in Google Scholar
130. Wishart DS, Arndt D, Berjanskii M, Tang P, Zhou J, Lin G. CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data. Nucleic Acids Res 2008;36:W496–502.10.1093/nar/gkn305Search in Google Scholar
131. Rykunov D, Fiser A. New statistical potential for quality assessment of protein models and a survey of energy functions. BMC Bioinformatics 2010;11:128.10.1186/1471-2105-11-128Search in Google Scholar
132. Rykunov D, Steinberger E, Madrid-Aliste CJ, Fiser A. Improved scoring function for comparative modeling using the M4T method. J Struct Funct Genomics 2009;10:95–9.10.1007/s10969-008-9044-9Search in Google Scholar
133. Rykunov D, Fiser A. Effects of amino acid composition, finite size of proteins, and sparse statistics on distance-dependent statistical pair potentials. Proteins 2007;67:559–68.10.1002/prot.21279Search in Google Scholar
134. Lazaridis T, Karplus M. Effective energy function for proteins in solution. Proteins 1999;35:133.10.1002/(SICI)1097-0134(19990501)35:2<133::AID-PROT1>3.0.CO;2-NSearch in Google Scholar
135. Morozov AV, Kortemme T. Potential functions for hydrogen bonds in protein structure prediction and design. Adv Protein Chem 2005;72:1–38.10.1016/S0065-3233(05)72001-5Search in Google Scholar
136. Fiser A, Sali A. Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol 2003;374:461.10.1016/S0076-6879(03)74020-8Search in Google Scholar
137. Zemla A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 2003;31:3370–4.10.1093/nar/gkg571Search in Google Scholar
138. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, et al. BioMagResBank. Nucleic Acids Res 2008;36:D402–8.10.1093/nar/gkm957Search in Google Scholar
139. Andreeva A, Howorth D, Chandonia JM, Brenner SE, Hubbard TJ, Chothia C, et al. Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 2008;36:D419–25.10.1093/nar/gkm993Search in Google Scholar
140. Remmert M, Biegert A, Hauser A, Soding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 2012;9:173–5.10.1038/nmeth.1818Search in Google Scholar
141. Altschul SF, Koonin EV. Iterated profile searches with PSI-BLAST – a tool for discovery in protein databases. Trends Biochem Sci 1998;23:444.10.1016/S0968-0004(98)01298-5Search in Google Scholar
142. Baber JL, Libutti D, Levens D, Tjandra N. High precision solution structure of the C-terminal KH domain of heterogeneous nuclear ribonucleoprotein K, a c-myc transcription factor. J Mol Biol 1999;289:949–62.10.1006/jmbi.1999.2818Search in Google Scholar PubMed
143. Koga N, Tatsumi-Koga R, Liu GH, Xiao R, Acton TB, Montelione GT, et al. Principles for designing ideal protein structures. Nature 2012;491:222.10.1038/nature11600Search in Google Scholar PubMed PubMed Central
©2014 by De Gruyter
